At the end of the course, you will be able to:
- Assess the prediction properties of the supervised learning methods covered in class;
- Correctly use regularization to improve predictions from linear models, and also to identify important explanatory variables;
- Explain the practical difference between predictions obtained with parametric and non-parametric methods, and decide in specific applications which approach should be used;
- Select and construct appropriate ensembles to obtain improved predictions in different contexts;
- Use and interpret principal components and other dimension reduction techniques;
- Employ reasonable coding practices and understand basic R syntax and function.
- Write reports and use proper version control; engage with standard software.